Data-driven methodologies proposed for financial time series analysis in general, and LOB(Limit Order Book)-based stock mid-price prediction in particular, can be divided into two major categories, i.e. those built on statistical models and data driven models. Methodologies based on statistical models study the statistical properties of the instances forming the time-series data [1]}. A comprehensive survey on statistical methods and stochastic modeling for LOB data can be found in [2]}, and a mathematical study of the order book as a multidimensional continuous-time Markov chain is presented in [3]}. The underlying assumption of statistical models used for time-series data analysis is that the data is generated by a linear process [4]}. Such assumption limits their ability to model real world data, as these are highly nonlinear and non-stationary.
